Power Flow Balance Research Articles

Studying the Optimal Frequency Control Condition for Electric Vehicle Fast Charging Stations as a Dynamic Load Using Reinforcement Learning Algorithms in Different Photovoltaic Penetration Levels

This study investigates the impact of renewable energy penetration on system stability and validates the performance of the (Proportional-Integral-Derivative) PID-(reinforcement learning) RL control technique. Three scenarios were examined: no photovoltaic (PV), 25% PV, and 50% PV, to evaluate the impact of PV penetration on system stability. The results demonstrate that while the absence of renewable energy yields a more stable frequency response, a higher PV penetration (50%) enhances stability in tie-line active power flow between interconnected systems. This shows that an increased PV penetration improves frequency balance and active power flow stability. Additionally, the study evaluates three control scenarios: no control input, PID-(Particle Swarm Optimization) PSO, and PID-RL, to validate the performance of the PID-RL control technique. The findings show that the EV system with PID-RL outperforms the other scenarios in terms of frequency response, tie-line active power response, and frequency difference response. The PID-RL controller significantly enhances the damping of the dominant oscillation mode and restores the stability within the first 4 s—after the disturbance in first second. This leads to an improved stability compared to the EV system with PID-PSO (within 21 s) and without any control input (oscillating more than 30 s). Overall, this research provides the improvement in terms of frequency response, tie-line active power response, and frequency difference response with high renewable energy penetration levels and the research validates the effectiveness of the PID-RL control technique in stabilizing the EV system. These findings can contribute to the development of strategies for integrating renewable energy sources and optimizing control systems, ensuring a more stable and sustainable power grid.

Two-Way Power Flow Balancing in Three-Phase Three-Wire Networks by Unbalanced Capacitive Shunt Compensation

Developed to achieve the balancing of three-phase loads and improve their power factor, the BCC (Balancing Capacitive Compensator) is presented in this paper as having a broader capability, namely that of balancing the two-way power flow. BCCs are becoming useful in today’s distribution networks with a high content of DERs (Distributed Energy Resources), where unbalanced power transfers to the higher voltage network occur more and more frequently as a result of the excess power generated. The article contains a case study in which, by means of Matlab-Simulink 2021 modelling, such a network is studied by considering two regimes corresponding to the two-way power flow. The numerical analysis of phase components and sequence components confirms the validity of the mathematical model concerning the BCC and also for the case of changing the way of power flow in the section controlled by the compensator. This demonstrates the possibility of extending the load balancing function of the BCC to that of balancing the two-way power flow and is an additional argument in support of replacing, in the more or less near future, conventional shunt capacitive compensators with capacitive balancing compensators.

Multi-objective optimization and sustainable design: a performance comparison of metaheuristic algorithms used for on-grid and off-grid hybrid energy systems

Alternative energy sources are needed for a sustainable world due to rapidly increasing energy consumption, fossil fuels, and greenhouse gases worldwide. A hybrid renewable energy system (HRES) must be optimally dimensioned to be responsive to sudden load changes and cost-effective. In this study, the aim is to reduce the carbon emissions of a university campus by generating electricity from a hybrid energy production system with solar panels, wind turbine, a diesel generator, and battery components. On the university campus where the hybrid energy system will be installed, the ambient temperature, solar radiation, wind speed, and load demands have been recorded in our database. Optimization algorithms were used to select the power values of the system components to be installed using these data in an efficient and inexpensive manner according to the ambient conditions. For optimal sizing of HRES components, gray wolf optimizer combined with cuckoo search (GWOCS) technique was investigated using MATLAB/Simulink. In this way, it has been tried to increase their efficiency by combining current optimization techniques. The cornerstone of our optimization efforts for both on-grid and off-grid models pivots on a constellation of critical decision variables: the power harvested from wind turbines, the productivity of solar panels, the capacity of battery storage, and the power contribution of diesel generators. In our pursuit of minimizing the annual cost metric, we employ a tailor-made function, meticulously upholding an array of constraints, such as the quotient of renewable energy and the potential risk of power disruption. A robust energy management system is integral to our design, orchestrating the delicate power flow balance among micro-grid components—vital for satisfying energy demand. Upon analyzing the outcomes of the study, it is apparent that the proposed Scenario 1 HRES effectively utilizes solar and battery components within the off-grid model, surpassing the efficiency of four other hybrid scenarios under consideration. Regarding optimization processes, the off-grid model exhibits superior results with the implementation of the GWOCS algorithm, delivering faster and more reliable solutions relative to other methodologies. Conversely, the optimization of the on-grid model reaches its optimal performance with the application of the cuckoo search algorithm. A comprehensive comparison from both technical and economic view points suggests the on-grid model as the most feasible and suitable choice. Upon completion of the optimization process, the load demand is catered to by a combination of a 2963.827-kW solar panel, a 201.8896-kW battery, and an additional purchase of 821.9 MWh from the grid. Additionally, an energy surplus sale of 1379.8 MWh to the grid culminates in an annual cost of system (ACS) of 475782.8240 USD, a total net present cost of 4815520.2794 USD, and a levelized cost of energy of 0.12754 USD/kWh. Solar panels cover the entire system, and the renewable energy fraction is 100%.

Deep Q-Learning-Based Mud Ring Optimization Approach for Optimal Power Flow in Islanded DC Microgrid

Microgrid (MG) is the basic element that recreates a significant role in integrating renewable energy sources (RES). Direct current (DC) MG has advantages over alternating current (AC) for many applications. A DC-MG is connected to the islanded mode through power electronic converters, which are also an important component for RES integration. Due to environmental factors, RES will struggle to load the conditions. Branches of DC-MG have battery energy storage systems (BESSs) installed for compensating the supply–load imbalance. Even though BESS are installed, accurate maintenance is still required to maintain a balanced power flow. This work proposes an adaptive deep Q-learning (DQL) based mud ring optimization technique (DQMR) for optimal power flow in islanded DC-MG. DQL is effective for the task with discrete and low-dimensional action spaces, and mud ring algorithm (MRA) is to clear up large-scale optimization problems and power flow problems. The implementation is done through MATLAB/Simulink model. The error is minimized to 6% by this method, and the losses are also reduced. IEEE-9 bus system and IEEE-30 bus system are used in the proposed work to validate the performance.

Indirect measurement method for high frequency response of complex structure based on statistical energy analysis

The harsh working environments often make it difficult to measure high frequency responses of complex structures in the fields such as aerospace and aeronautic, which severely hinders the evaluation of structural performance and safety. An indirect measurement method is proposed to estimate the high frequency responses of non-measurable subsystems using a statistical energy analysis (SEA) framework. Whether or not the non-measurable subsystem includes input load, two indirect measurement approaches are developed by constructing an overdetermined problem and a well-posed problem respectively. Both utilize only the power flow balance equations of unexcited subsystems to solve the problems and thus do not require solving for input loads. The approaches are validated numerically and experimentally through case study of a rocket engine structure. Furthermore, the interval error analyses indicate that the approaches are robust against SEA modeling errors and response measuring errors.

Effects of Distributed Generation on Carbon Emission Reduction of Distribution Network

Increasing renewable energy integration in power systems is an important way of decarbonising carbon emissions. Recently, the ever-increasing deployment of distributed generation (DG) is considered effective in reducing carbon emissions and power loss, such as wind, photovoltaic (PV), and combined heat and power generation (CHP) on the demand side. Thus, the evaluation of carbon emission flow (CEF) will be a crucial factor for distribution network planning with the integration of DGs, which may act as a supplemented indicator in addition to traditional power flow study. In the planning stage, it is paramount to ensure that decarbonisation process of the power distribution system is in line with environmental and technical targets. Thus, the paper proposes a modelling strategy to combine the power flow and carbon emission flow. It aims to analyse and calculate the CEF based on the power-flow study. The novel model satisfies the power flow and CEF balance and can be directly used to evaluate the decarbonization of power system. The results of this study can help relevant energy decision-makers to do appropriate renewable energy generation planning and guide the power system to achieve carbon neutrality.

Transient stability enhancement in renewable energy integrated multi-microgrids: A comprehensive and critical analysis

Multi-microgrids offer various benefits including the decreased overloading of a single microgrid, more options during faulty conditions, and more utilization of renewable energy resources. However, the implementation of a multi-microgrid brings the challenges such as power system complexity, interconnection issues, bidirectional power flow management, and power flow balancing. In the presence of these challenges, the power flow stability of the multi-microgrids is a challenging problem. In this context, this study evaluates a transient stability analysis model in multi-microgrids using solar photovoltaics, wind power, and a unified power flow controller (UPFC). UPFC offers a more robust power flow control strategy compared with other flexible alternating current transmission systems (FACTS) devices. First, a multi-microgrid structure consisting of the two microgrids was designed in DIgSILENT PowerFactory software. Second, the load flow calculation was performed in the absence and presence of UPFC, short circuit fault, and grid connection. Third, the electromagnetic transients (EMT) simulation was performed for all these situations. The results exhibited that the UPFC would offer significant power flow stability in the multi-microgrids. It was observed that the UPFC resulted in more transient stability in the microgrid where it was located. However, it improved the power flow quality at all the locations in the multi-microgrids. In addition, UPFC offered significant transient stability during the fault occurrence. The results offer various insights into power flow management in multi-microgrids.

A Dispatching Method for Large-Scale Interruptible Load and Electric Vehicle Clusters to Alleviate Overload of Interface Power Flow

The study of dispatching methods for large-scale interruptible loads and electric vehicle clusters is of great significance as an optional method to alleviate the problem of overload in interface power flow. In this paper, the distribution model and transfer capacity of large-scale interruptible load and electric vehicle in two dimensions of time and space were firstly introduced. Then, a large-scale interruptible load and electric vehicle dispatching model considering transmission interface power flow balance was established. Finally, a case study was carried out with the city power grid as the research object. Studies show that by dispatching large-scale interruptible load and electric vehicle, the overload rate of interface power flow can be reduced by 12–17%, while the proportion of clean energy generation increased by 4.19%. Large-scale interruptible load and electric vehicles are quite different in terms of the role they play in grid regulation. The regulation cost of electric vehicles is higher than that of large-scale interruptible load, but it also has the advantages of promoting the consumption of clean energy and improving the overall operating economy. Which type of resource should be given priority is based on the actual state of the grid. In addition, the cost of electricity has a significant impact on the load response behavior of electric vehicles. It should be determined according to various factors, such as interface power flow control requirements, regulation costs, and power grid operation costs.

Distribution Network Balance Method under High Proportion of Distributed Power Penetration Scenario

With the rapid development of various subjects represented by distributed generation, how to implement power balance in a distribution network has become a research direction. The utilization efficiency of a distribution network can be improved by the bidirectional power flow of flexible resources. However, the traditional distribution network power balance method relies on historical data, making it challenging to determine the principle of new subject participation. It does not meet the requirements of bidirectional power flow balance and loses critical information such as complementary load characteristics. We believe that distribution network power prediction needs to transform from single load prediction to multi-resource prediction, from quantitative prediction to quantitative plus characteristic prediction. In comparison to the traditional methods, curve superposition can be better compatible with the research results of different new load forecasting topics and meet the needs of efficiency and precise network planning.

Reactive power control of solar photovoltaic inverters for grid code compliance support

The compensation of reactive power in smart inverters is one solution to address the issue of voltage violations in the distribution network due to the penetration of solar photovoltaic power generation. However, options for reactive power control are limited during variations in irradiation and daily load on the feeder. This study aims to investigate the performance difference between four reactive power control techniques including Q(V) control, Q(P) control, fixed Q-Var, and fixed power factor (PF) available in smart inverters to reduce voltage violations due to PV integration and comply with the grid-code. Three-phase balanced power flow was simulated in a medium voltage distribution network (MVDN) considering the reactive power control mode of the inverter under variations in solar radiation and daily load. The results showed that the Q(V) control was more effective in improving distribution feeder voltage than other techniques and showed its compliance with the grid-code. The limiting setting point for var injection or power factor limit should be proportional to the daily grid load profile.

Cascading Failure Propagation and Mitigation Strategies in Power Systems

In this article, we investigate the local and nonlocal failure propagation patterns in power systems and propose effective mitigation strategies. It is widely acknowledged that the sequence of failure events of component overloading is determined by power flow redistribution. To understand the power flow redistribution process, we analyze the prefault power flow condition using network flow theory and by clustering the system into several regions with balanced local power flow. We observe that faults triggering local power imbalance would lead to remote power transfer and faults causing no local power imbalance would lead to local power redistribution. The improved understanding of the failure process permits us to develop effective mitigating strategies consisting of adaptive power balance restoration and selective edge protection for cascading failure propagation. In addition, we propose a set of indexes to measure the local and nonlocal propagation risks of cascading failures. Numerical simulations on the IEEE 118- and 300-bus systems demonstrate that the proposed mitigation strategies can significantly reduce the blackout sizes and are tolerant to incorrect transmission line tripping.

Power Flow Balancing With Decentralized Graph Neural Networks

We propose an end-to-end framework based on a Graph Neural Network (GNN) to balance the power flows in energy grids. The balancing is framed as a supervised vertex regression task, where the GNN is trained to predict the current and power injections at each grid branch that yield a power flow balance. By representing the power grid as a line graph with branches as vertices, we can train a GNN that is accurate and robust to changes in topology. In addition, by using specialized GNN layers, we are able to build a very deep architecture that accounts for large neighborhoods on the graph, while implementing only localized operations. We perform three different experiments to evaluate: i) the benefits of using localized rather than global operations and the tendency of deep GNN models to oversmooth the quantities on the nodes; ii) the resilience to perturbations in the graph topology; and iii) the capability to train the model simultaneously on multiple grid topologies and the consequential improvement in generalization to new, unseen grids. The proposed framework is efficient and, compared to other solvers based on deep learning, is robust to perturbations not only to the physical quantities on the grid components, but also to the topology.

Identification of multiple nonlinear lap joints using instantaneous power flow balance

PurposeThe purpose of this paper is to apply the novel instantaneous power flow balance (IPFB)-based identification strategy to a specific practical situation like nonlinear lap joints having single and double bolts. The paper also investigates the identification performance of the proposed power flow method over conventional acceleration-matching (AM) methods and other methods in the literature for nonlinear identification.Design/methodology/approachA parametric model of the joint assembly formulated using generic beam element is used for numerically simulating the experimental response under sinusoidal excitations. The proposed method uses the concept of substructure IPFB criteria, whereby the algebraic sum of power flow components within a substructure is equal to zero, for the formulation of an objective function. The joint parameter identification problem was treated as an inverse formulation by minimizing the objective function using the Particle Swarm Optimization (PSO) algorithm, with the unknown parameters as the optimization variables.FindingsThe errors associated with identified numerical results through the instantaneous power flow approach have been compared with the conventional AM method using the same model and are found to be more accurate. The outcome of the proposed method is also compared with other nonlinear time-domain structural identification (SI) methods from the literature to show the acceptability of the results.Originality/valueIn this paper, the concept of IPFB-based identification method was extended to a more specific practical application of nonlinear joints which is not reported in the literature. Identification studies were carried out for both single-bolted and double-bolted lap joints with noise-free and noise-contamination cases. In the current study, only the zone of interest (substructure) needs to be modelled, thus reducing computational complexity, and only interface sensors are required in this method. If the force application point is outside the substructure, there is no need to measure the forcing response also.

Research on the Economic Dispatch of Power Systems with a High Proportion of Renewable Energy based on Improved PSO

The penetration rate of renewable energy sources represented by wind power and photovoltaics in the power grid is increasing, adding complexity to unit dispatch. In this paper, a multi-objective optimization model is established for power systems containing wind power and photovoltaic economic dispatch. Firstly, the power generation cost of traditional units, the emission cost of traditional units, the power generation costs of wind turbines and photovoltaics, and the output characteristics of wind power and photovoltaics are analyzed. Secondly, a multi-objective economic dispatch model with the lowest power generation cost, the smallest emission cost, and the smallest power generation cost of new energy units are established, and the power flow balance constraint, supply and demand balance constraint, unit output constraint, and node voltage constraint are considered. Then the improved particle swarm algorithm is proposed to solve the multi-objective function to obtain better convergence and optimal solution. Finally, the simulation is carried out in the IEEE-30 node system and the HRP-38 system, which compares and illustrates the similarities and differences of the solution results of various algorithms under multi-objective and verifies the effectiveness of the algorithm.

DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems

To cope with increasing uncertainty from renewable generation and flexible load, grid operators need to solve alternative current optimal power flow (AC-OPF) problems more frequently for efficient and reliable operation. In this article, we develop a deep neural network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional iterative solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized a prediction-and-reconstruction procedure in our previous studies, DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining ones by solving the power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to be predicted by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process toward guaranteeing the inequality constraints. We also drive a condition for tuning the DNN size according to the desired approximation accuracy, which measures its generalization capability. It provides theoretical justification for using DNN to solve AC-OPF problems. Simulation results for IEEE 30/118/300-bus and a synthetic 2000-bus test cases demonstrate the effectiveness of the penalty approach. They also show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art iterative solver, at the expense of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> 0.2% cost difference.

An Efficient Primal-Dual Interior-Point Algorithm for Volt/VAR Optimization in Rectangular Voltage Coordinates

Security and reliability of electrical power supply has become indispensable to modern society, and the system operator is challenged to manage the increasingly complex modern power system in a manner that ensures the expected reliability and security of system operation. In this context, Volt/VAR optimization (VVO) plays a key role in the efficient delivery of power through the transmission system, contributing significantly to the security, reliability, quality and economy of system operation. This article presents the design and implementation of an efficient primal-dual interior-point algorithm for the solution of the VVO problem. The primal-dual interior-point method combines efficient constraint handling by means of logarithmic barrier functions, Lagrangian theory of optimization, and the Newton method to constitute one of the most efficient deterministic algorithms for large-scale nonlinear optimization. The developed algorithm also incorporates the efficient Newton-Raphson load flow computation, which ensures that the solution is feasible with respect to the power flow balance equations at each iteration of the VVO algorithm. Both the VVO and Newton-Raphson load flow problems are formulated in the rectangular coordinates of system voltages. This is a departure from most researchers, who make use of the polar formulation, and adds considerably to the efficiency of the developed algorithm. The efficiency and effectiveness of the developed algorithm has been demonstrated by means of case studies performed on the 6-bus and IEEE 14-bus, 30-bus and 118-bus test systems, which have been selected to analyse the computational efficiency and scalability of the algorithm as it is applied to systems of various sizes. The extensive analyses that have been conducted reveal the developed primal-dual interior-point algorithm’s effectiveness and efficiency, particularly in being able to successfully solve the VVO problem for systems of widely varying sizes without disproportionate increase in computational cost or deterioration in the quality of the results. The developed algorithm exhibits characteristics of fast convergence, high efficiency, and scalability to large-scale problems.

Stability Analysis and Control Design for Multiterminal HVDC Network With Power Flow Controller

The power flow control in high-voltage direct current (HVDC) grids can be improved by employing the interline dc power flow controller (IDCPFC). This article presents an improved control scheme using IDCPFC to balance power flow in the dc networks. In this work, a differential-mode transformer (DMT) is used in combination with a bidirectional buck converter to form a variable DMT. In comparison to the existing methods, the proposed system is scalable to higher voltages and powers, which makes it suitable for high-voltage dc transmission systems. Analysis of a small-signal model of the proposed method and design of compensator for improvement of transient behavior is performed. A simulation is performed with independent feed-forward and feedback control to show the effectiveness of the proposed method. The proposed method is also validated through a 600-W experimental prototype. The experimental results show that the current flow between lines is controlled for unbalance load conditions.

Performance dynamics improvement of a hybrid wind/fuel cell/battery system for standalone operation

The present study is concerned with improving the dynamics of a hybrid generation system utilized for feeding an isolated load. The system under study consists of a wind-driven synchronous generator with permanent magnet type, a fuel cell stack and a storage battery layout used to enhance the system reliability. A detailed design for all system parts is introduced. A new formulated predictive controller is utilized to enhance the performance of synchronous generator in comparison with traditional controllers. The wind turbine power system is designed and adopted a maximum power point tracking (MPPT) strategy to optimally exploit the captured wind energy. An energy management procedure is also considered to balance the power-sharing between different system units. Extensive performance evaluation analysis is introduced in order to validate the capability of the designed controllers of the generator and fuel cell and check the feasibility of the energy management strategy (EMS) as well. The obtained results approve the capability of the proposed controller with the synchronous generator in achieving better dynamics compared with traditional schemes and confirm the validity of the fuel cell control system in managing the stack power. The results also approve the effectiveness of the designed EMS in preserving a balanced power flow.

Distribution network reconfiguration using time-varying acceleration coefficient assisted binary particle swarm optimization

The particle swarm optimization (PSO) algorithm is widely used to solve a variety of complicated engineering problems. However, PSO may suffer from an effective balance between local and global search ability in the solution search process. This study proposes a new acceleration coefficient for the PSO algorithm to overcome this issue. The proposed coefficient is implemented on the distribution network reconfiguration (DNR) problem to reduce power loss. The lowest power loss is obtained while problem constraints (maintain radial structure, voltage limits, and power flow balance) are satisfied with the proposed method. The validity of the proposed acceleration coefficient-based binary particle swarm optimization (BPSO) in handling the DNR problem is examined through simulation studies on IEEE 33-bus, P&G 69-bus, and 84-bus Taiwan Power Company (TPC) practical distribution networks. Furthermore, the DNR problem is evaluated regarding energy cost and environmental issues. Finally, the average computational times of the different acceleration coefficient-based PSO methods are compared. The solution speed of the proposed acceleration coefficient-based method is faster than the other methods in the DNR problem.

An Optimized PI Controller-Based SEPIC Converter for Microgrid-Interactive Hybrid Renewable Power Sources

With the use of hybrid renewable sources for example solar and wind turbines, an autonomous electric power generation system based on self-sufficient electric power generation is built in order to promote a smart and ecologically friendly environment. The three-phase inverter that links this scattered generating unit to the grid is in charge of ensuring that it is properly connected to the grid. While the energy produced by the hybrid unit is being utilized, it is also being stored in the batteries so that it may be used to transport power when other sources of power are not available, such as when the grid is down. This stand-alone power conversion and storage system is being built with the aid of power electronic converters and controllers, among other components, in order to ensure balanced power flow operation. To produce PWM pulses for the generator side converter, a PI controller is utilized. On the PV side, an improved PI controller is used to drive the SEPIC converter, which increases the transient responsiveness of the converter while it is being controlled by the controller. In order to communicate with the grid, the generated electricity is routed via a three-phase inverter that is controlled according to the DQ theory. The challenges connected with poor power quality are substantially resolved by the converters that have been presented. For simplicity of use, an automated control function has been introduced; in addition, an interactive MATLAB model of the system is being developed for minor and medium-scale microgrid applications; also, a prototype is being created to verify the simulation results.